Macros
AuraScore 81/100

Multimodal Safety Filter Appeal and Remediation Macro Framework

Standardize policy appeal responses and safe prompt refactoring for false-positive safety filter triggers in multimodal platforms.

Implement this framework to equip Trust and Safety and Customer Support teams with standardized macro trees for handling image generation safety blocks, automated filter appeals, and safe prompt reconstruction.

Template

Role: Lead Trust and Safety Operations Engineer directing multimodal policy enforcement and customer remediation workflows.

Context

  • Moderation architecture layers: {{moderation_filter_tiers}}
  • Maximum appeal turnaround: {{appeals_turnaround_target}}
  • Supported input modalities: {{multimodal_input_modalities}}
  • Legal escalation criteria: {{escalation_legal_thresholds}}
  • Customer trust scoring: {{user_risk_classification}}
  • Communication tone standard: {{macro_tone_guidelines}}

Task

Construct a comprehensive support macro framework for processing multimodal safety filter triggers, facilitating swift false-positive appeals, and providing compliant prompt refactoring instructions while adhering strictly to platform safety guidelines.

Method

  1. Categorize moderation trigger events across {{moderation_filter_tiers}} to distinguish between overt violations and semantic false positives.
  2. Design initial acknowledgment macros that set clear expectations based on {{appeals_turnaround_target}}.
  3. Create diagnostic response paths depending on the flagged modality within {{multimodal_input_modalities}} (text prompt, input image, control net mask).
  4. Build policy-compliant prompt refactoring templates that show users how to express benign creative concepts without triggering lexical tripwires.
  5. Establish automated branching macros that deflect high-risk violations while providing educational guidance to benign users categorized by {{user_risk_classification}}.
  6. Formulate strict escalation macros for incidents crossing {{escalation_legal_thresholds}}.
  7. Harmonize all macro variants with {{macro_tone_guidelines}} to ensure neutral, empathetic, and non-accusatory communication.

Constraints

  • MUST maintain an objective, non-judgmental tone across all appeal outcomes.
  • MUST NOT reveal exact proprietary filter thresholds, regex strings, or internal safety classifier boundaries.
  • Refactored prompt recommendations must guarantee zero probability of tripping secondary safety filters.
  • High-risk violations must immediately bypass macro remediation and follow legal isolation protocols.

Output format

  • Macro Routing Decision Tree (Triage based on {{user_risk_classification}})
  • 3 Core Appeal Macro Templates (False-Positive Clearance, Safe Refactoring Guide, Firm Policy Uphold)
  • Modality-Specific Parameter Guidance Table (Targeting {{multimodal_input_modalities}})
  • Internal QA Checklist for Support Representatives Total length: 450-650 words.

Self-review

  1. Are internal moderation secrets protected while still offering clear user guidance?
  2. Does the framework provide distinct paths for all modalities in {{multimodal_input_modalities}}?
  3. Are escalation thresholds aligned with {{escalation_legal_thresholds}}?
AuraScore breakdown
81/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

support-success
support-macros
image-multimodal-prompting
trust-and-safety
moderation-macros
image-generation